Principal Machine Learning Engineer

MicrosoftRedmond, WA
9dHybrid

About The Position

As a Principal Machine Learning Engineer, you will work on the Data Labeling and classification on large scale multi modal Copilot data part of the Microsoft AI (MAI) organization. We’re looking for a hands-on ML engineer to prototype and productionize complex classification flows on real production logs, operate prompted classifiers at scale (ad hoc and scheduled), and build secure, compliant data-labeling pipelines. We’re looking for someone with experience in data pipelines, data science, and machine learning, as well as a strong communicator and great teammate. The right candidate takes the initiative and enjoys building world-class consumer experiences and products in a fast-paced environment. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location. This expectation is subject to local law and may vary by jurisdiction.

Requirements

  • Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • OR equivalent experience.

Nice To Haves

  • 7+ years' experience writing production-quality Python or Java or Scala code.
  • 5+ years' experience in distributed systems design and implementation of large scale data processing systems
  • 3+ years' experience building ML data pipelines using atleast one of AML, Promptflow, Langchain or LangGraph
  • Demonstrated interest in Responsible AI.
  • Experience prompting, evaluating, and working with large language models.

Responsibilities

  • Build evaluation loops (precision/recall, calibration, drift, human-in-the-loop) and publish dashboards/SLOs.
  • Generalize machine learning (ML) solutions into repeatable frameworks.
  • Operationalize prompted classifiers at scale (batch & streaming), including orchestration, autoscaling, monitoring, and cost guardrails.
  • Conduct thorough review of data analysis and techniques used to summarize the process review and highlight areas that have been missed or need re-examining.
  • Collaborate cross-functionally with DS, Security, and Platform to define schemas, access patterns, and governance.
  • Independently write efficient, readable, extensible code and model pipelines.
  • Commit to a customer-oriented focus by acknowledging customer needs and perspectives, validating customer perspectives, focusing on broader customer context, and serving as a trusted advisor.
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